WHY DOES ONTARIO REQUIRE EQUAL TREATMENT IN SALES OF CORPORATE CONTROL?
Bibliographic record
Abstract
There is a long-standing controversy over the question of whether controlling and minority shareholders should be treated equally in sales of control. Ontario securities law adopts a mandatory ‘equal opportunity rule’ that requires acquirers in most cases to extend a premium offer to purchase controlling shares to minority shareholders and controlling shareholders on equal terms. This article concludes that, having regard to theory, empirical evidence, and the specific rules in place, the most coherent explanation for Ontario's mandatory approach is that it assists target shareholders in extracting gains from acquirers of control. As a matter of theory, there is no need for a mandatory rule if the purpose of the rule is to deter inefficient sales of control to buyers interested in diverting value from the minority, but a mandatory rule makes sense if the purpose is to increase the purchase price of control blocks. The extraction hypothesis is consistent with existing empirical evidence, as well as with this article's event study based on the possible sale of control of Canadian Tire in the 1980s (the case that provided the impetus for the mandatory equal opportunity rule we observe today in Ontario). Finally, the particulars of the rule in place, such as the exemption for firms existing when the rule was imposed, are consistent with the extraction theory but not with other theories, especially a ‘fairness’ theory of equal treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".